Abstract
The intermittency and uncertainty of wind power result in challenges for large-scale wind power integration. Accurate wind power prediction is becoming increasingly important for power system planning and operation. In this paper, a probabilistic interval prediction method for wind power based on deep learning and particle swarm optimization (PSO) is proposed. Variational mode decomposition (VMD) and phase space reconstruction are used to pre-process the original wind power data to obtain additional details and uncover hidden information in the data. Subsequently, a bi-level convolutional neural network is used to learn nonlinear features in the pre-processed wind power data for wind power forecasting. PSO is used to determine the uncertainty of the point-based wind power prediction and to obtain the probabilistic prediction interval of the wind power. Wind power data from a Chinese wind farm and modeled wind power data provided by the United States Renewable Energy Laboratory are used to conduct extensive tests of the proposed method. The results show that the proposed method has competitive advantages for the point-based and probabilistic interval prediction of wind power.
Highlights
Due to limitations associated with conventional energy use and increasing environmental issues, wind energy is widely implemented because it represents a source of green renewable energy [1,2].The main use of wind energy is power generation, converting wind energy into electricity
In order to verify the advantages advantages of the VPBC (VMD + phase space reconstruction (PSR) + bi-level convolutional neural networks (CNNs)) + particle swarm optimization (PSO) proposed in this paper, the performance of the VPBC (VMD + PSR + bi-level CNN) + PSO proposed in this paper, the performance of point of point prediction and interval prediction were compared and verified, respectively
Based forecasting results with the persistence method, CNN, and VPCB (VMD + PSR + CNN-back-propagation neural network (BPNN)), CNN-BPNN), the interval prediction results were compared with CNN + PSO, VPCB + PSO
Summary
Due to limitations associated with conventional energy use and increasing environmental issues, wind energy is widely implemented because it represents a source of green renewable energy [1,2]. The aforementioned forecasting methods have improved the accuracy of wind power forecasting to varying degrees, they are all shallow learning models, regardless of whether they are time series methods, machine learning methods, or hybrid methods. CNN wind power forecasting model that combines variational mode decomposition (VMD) and PSR is proposed in this study This model takes advantage of the deep-feature extraction of the CNN to improve the accuracy of wind power forecasting. The wind power data is preprocessed using VMD and PSR to obtain data that are better suited for CNNs. A forecasting model based on a bi-level CNN and PSO is developed; the model makes full use of the characteristics of CNNs to extract deep features and obtain the probabilistic forecasting interval via PSO.
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